Power BI Data Engineer — Enterprise Analytics

5 days ago

Chennai, Tamil Nadu, India Accel4 Inc Full time ₹ 15,00,000 - ₹ 25,00,000 per year

Power BI Data Engineer — Enterprise Analytics

Location(s): Chennai, Hyderabad, Bangalore, India (On-site)

About the Role

Design, develop, and maintain interactive Power BI dashboards and reports to empower business stakeholders with actionable insights.

Responsibilities

  • Build and optimize ETL pipelines for data ingestion, transformation, and loading into analytics platforms (Power BI, Snowflake, Databricks, or Microsoft Fabric).
  • Develop and implement star schemas, data models, and semantic models for analytics cubes to support enterprise reporting and BI needs.
  • Collaborate with data scientists, analysts, and business teams to translate requirements into scalable data solutions.
  • Ensure data quality, governance, and security across all BI workflows, adhering to best practices.
  • Deploy and monitor data solutions on cloud platforms (Azure preferred, AWS/GCP a plus).
  • Continuously improve performance of data pipelines and reports through optimization and automation.

Qualifications

  • 5–7 years of experience in data engineering, with a focus on BI solutions and Power BI development.

Required Skills

  • Strong expertise in Power BI (DAX, Power Query, report design) and building intuitive, high-performance dashboards.
  • Proven experience designing and implementing ETL processes and star schemas for analytics cubes.
  • Hands-on experience with cloud data platforms like Snowflake, Databricks, or Microsoft Fabric.
  • Proficiency in SQL for querying and transforming large datasets.
  • Familiarity with Azure data services (e.g., Azure Data Factory, Synapse Analytics) or equivalent cloud tools.
  • Self-motivated with a proactive approach to problem-solving, capable of managing complex projects with minimal oversight.

Preferred Skills

  • Familiarity with data orchestration tools (e.g., Apache Airflow, Prefect).
  • Knowledge of data warehousing concepts and tools (e.g. Fabric, Redshift, BigQuery).
  • Exposure to AI/ML integration for predictive analytics or anomaly detection.

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